GMS 7795: Fundamentals of Computational Neuroscience

Spring 2008

 

NEW – This course will be offered via distance learning. Please contact the instructor if you are interested.

 

Instructor: Justin C. Sanchez, Ph.D. (http://nrg.mbi.ufl.edu)

Office hours: T, Th 1:40-2:40

 

Prerequisite: This course is open to all graduate students with an interest in Systems Neurophysiology, Neural Computation, Neural Engineering, and Experimental Neurophysiological Analysis. Only a basic knowledge of calculus and computing is required.

 

Class Meeting: T, Th 12:50-1:40, UFBI L4-101

Class Homepage: http://nrg.mbi.ufl.edu/courses/FCN/fcn_index.html

 

Required textbook: Fundamentals of Computational Neuroscience, Thomas P. Trappenberg, Oxford University Press. 2002. ISBN: 0-19-851582-0

 

Course Objectives: This course will present the major concepts of neural signaling and communication from the single neuron to systems of neural ensembles. We will discuss the role of neural computation for advancing knowledge of information-processing in the brain. It will be shown how experimental data can be summarized and predicted through computational modeling. Whenever possible, computer simulations will be used to provide real examples for student experimentation.

 

Grade Determination: 1/3 Homework, 1/3 midterm, 1/3 Final

 

Policies: Late policy for homeworks: 20% deducted per day, unless prior arrangements were made with the instructor. Students are encouraged to work together on the homework, but the work that is handed in must be individual work.

 

Schedule

 

Week 1.

 

            Lecture 1

Chapter 1. Introduction

á       Origins

á       What is a model?

á       Homework 1 (Read Chapter 1 all, 12.1 – Matlab Intro, Appendix A.1 – Matrix Algebra Primer)

á       Solution

á       Lecture 1

 

 

Week 2

 

Lecture 2

Chapter 2. Neurons and conductance-based models

á       Basic synaptic mechanisms

á       Generation of action potentials: Hodgkin-Huxley

á       Dendritic trees and the propagation of action potentials 

á       Lecture 2 (Read Chapter 2-3, 12.2.1, Appendix C1-3 Overview of HH, and Euler Integration, Homework 1)

Lecture 3

     Chapter 3. Spiking neurons and response variability

á       Integrate and fire

á       The spike-response model

á       Spike time variability

á       Homework 2

á       Solution (doc, m, m)

á       Lecture 3 (Read Chapter 3-4, Appendix B, supplementary paper, Homework 2)

 

 

Week 3.

Lecture 4

     Chapter 4a. Neurons in a network

á      Organizations of neuronal networks

á       Lecture 4 (Read Chapter 4-5)

Lecture 5

     Chapter 4b. Neurons in a network

á       Information transmission in networks

á       Population dynamics

á       Homework 3

á       Solution (doc, m, m)

á       Lecture 5 (Read Chapter 5, homework 3)

 

 

Week 4.

Lecture 6

     Chapter 5a. Representations and the neural code

á       How neurons communicate

á       Neural coding

á       Information theory

á       Lecture 6 (Read Chapter 5, supplementary paper, homework 3) 

Lecture 7

     Chapter 5b. Representations and the neural code

á       Population coding and decoding

á       Distributed representation

á       Lecture 7 (Read Chapter 5)

 

 

Week 5.

 

Midterm 

 

Lecture 8

     Chapter 6a. Feed-forward mapping networks

á       Perception, function representation, and look-up tables

á       Multilayer mapping networks

á       Lecture 8 (Read Chapter 6)

 

 

Week 6.

 

Lecture 9

Chapter 6b. Feed-forward mapping networks

á       Learning, generalization, and biological interpretations

á      Biological interpretations

á       Lecture 9 (Read Chapter 6)

 

 

Week 7.

 

Lecture 10

     Chapter 7. Associators and synaptic plasticity

á       Associative memory and Hebbian learning

á       The temporal structure of Hebbian plasticity: LTP and LTD

á       Homework 4 (Read Chapter 7, Appendix A.2)

á       Solution (doc, m)

á       Lecture 10 

Lecture 11

     Chapter 8. Auto-associative memory and network dynamics

á       Recurrent memory

á       Comparisons with hippocampus

á       Lecture 11(Read Chapter 8)

 

 

Week 8.

 

Lecture 12

á       Memory capacity

á       Dynamical Systems Intro

á       Homework 5 (Reach Chapter 10)

á       Solution (doc, m)

á       Lecture 12

Lecture 13

     Chapter 10a. Supervised learning and rewards systems

á       Supervised learning in motor systems

á       Lecture 13

 

 

Week 9.

 

Lecture 14

Chapter 10b. Supervised learning and rewards systems

á       Neural mechanisms in supervised learning

á       Reward Learning

á       Lecture 14

     Chapter 11a. System level organization

á       Large scale anatomical and functional organization

á       Modular mapping

 

 

Week 10.

 

Lecture 15

Chapter 11b. System level organization

á       Putting it all together (neurobiology, computation, modeling, systems theory, learning)

á       Brain-Machine Interfaces

á       Lecture 15

Final Exam

 

 

 

Academic Honesty

As a result of completing the registration form at the University of Florida, every student has signed the following statement: "I understand that the University of Florida expects its students to be honest in all their academic work. I agree to adhere to this commitment to academic honesty and understand that my failure to comply with this commitment may result in disciplinary action up to and including expulsion from the University." We agree to comply with the new Honor Code, which specifies that "We, the members of the University of Florida community, pledge to hold ourselves and our peers to the highest standards of honesty and integrity.